Set Up

Install/load necessary R packages

Set date range

min_year = 2017
max_year = 2021

Peatland Daily Water Table (Bogwell Data)

More information about peatland and upland water elevation data collected at the Marcell Experimental Forest can be found on the Environmental Data Initiative Repository Portal.

Manual Stripcharts

  • Read in the most up-to-date data from the following Box file path: External-MEF_DATA/Hydro/WaterTableElevation/Bogwells/L1

    • Less up-to-date, already published data can be found at the following Box file path: External-MEF_DATA/EDI/peatland_water_table_daily/edi.562.2/data_objects/

  • Clean the data into a tidy format

    • Subset data to 2017 - present
#set file path to read in data from Box 
filepath = "/Users/miaforsline/Library/CloudStorage/Box-Box/External-MEF_DATA/Hydro/WaterTableElevation/Bogwells/L1"

#read in the raw data 
bogwell <- read_csv(here(filepath, "L1_Peatland_well_daily_history_2021forMia.csv"))

#clean the data
##note: we do not want any E values for the flag column 
bogwell_clean <- bogwell %>% 
  clean_names() %>% 
  mutate(year = format(as.POSIXct(date, format = '%Y-%m-%d', 
                                  tz = "GMT"), 
                       format = '%Y'),
         year = as.numeric(year)) %>% 
  subset(year >= min_year & year <= max_year)

#save clean data CSV to use in future RMD files
write.csv(x = bogwell_clean, 
          file = file.path(here("intermediate_data", "bogwell_clean.csv")),
          row.names = FALSE)

S2 Manual Stripchart

bogwell_clean_s2 <- bogwell_clean %>% 
    subset(peatland == "S2")

#plot
p_bogwell_s2 <- ggplot(data = bogwell_clean_s2) + 
  geom_line(aes(x = date, 
                y = wte, 
                group = 1,
                text = paste0("Date: ", date, "\n",
                              "Water Table Elevation (m): ", wte))) + 
  theme_classic() + 
  labs(x = "Time", 
       y = "Water Table Elevation (m above sea level)",
       title = paste0("S2 Daily Peatland Water Table Elevation (", min_year, "-", max_year, ")") 
       ) + 
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5, 
                                     size = 7))

#interactive plot 
ggplotly(p_bogwell_s2,
         tooltip = "text")
bogwell_clean_s2 %>% 
  #remove rows that have NA wte values 
  filter(!is.na(wte)) %>% 
  summarise(mean_wte_m = round(mean(wte), digits = 3),
            max_wte_m = max(wte),
            min_wte_m = min(wte), 
            sd_wte_m = round(sd(wte), digits = 3)
            ) %>% 
  #t() %>% 
  kable(caption = "S2 Stripchart Bogwell Statistics", 
        col.names = c("Mean WTE (m)", 
                      "Max WTE (m)",
                      "Min WTE (m)",
                      "SD")) %>% 
  kable_material(c("striped", "hover"))
S2 Stripchart Bogwell Statistics
Mean WTE (m) Max WTE (m) Min WTE (m) SD
421.88 422.15 421.48 0.095

Shaft Encoders

Function for loading Campbell DataLogger .dat files:

readCDL = function(file){
  
  # read data file starting on 5th line
  dat <- read.csv(file, sep=",",header=FALSE,skip=4,na.strings="NAN",stringsAsFactors=F)
  
  # Read in just the header line (l2)
  # unlist the line, and remove quotes 
  h <- readLines(file, n=2)[2]
  n <- as.factor(unlist(strsplit(h, ",")) )
  n2 <- gsub('"', "", n)
  
  # assign column names to dataframe
  colnames(dat) = n2
  
  return(dat)
}

Constants to convert between feet and meters:

MetersPerFoot = 0.3048
FeetPerMeter = 3.28048

min_year = 2020
max_year = 2021

  • Read in the data from the following Box file path: External-MEF_DATA/DataDump/RealTimeData

  • Clean the data into a tidy format

    • Subset data to 2020 - present

S2 Shaft Encoder

  • We are interested in the MaxWTElev column
#set file path to read in data from Box 
filepath = "/Users/miaforsline/Library/CloudStorage/Box-Box/External-MEF_DATA/DataDump/RealTimeData"

#read in the raw data 
s2_se <- readCDL(file = here(filepath, "S2_bogwell_S2BW_Daily.dat"))

s2_se_clean <- s2_se %>% 
  clean_names() %>% 
  #select relevant columns
  select(timestamp,
         max_wt_elev) %>% 
  #rename columns 
  rename(datetime = timestamp,
         max_wt_elev_ft = max_wt_elev) %>% 
  #reformat columns into appropriate classes 
  mutate(datetime = as.POSIXct(datetime, format = '%Y-%m-%d %H:%M:%S', 
                                  tz = "GMT"),
    max_wt_elev_ft = as.numeric(max_wt_elev_ft),
    max_wt_elev_m = max_wt_elev_ft * MetersPerFoot)
  • Plot
#plot 
p_s2 <- ggplot(data = s2_se_clean) + 
  geom_line(aes(x = datetime, 
                y = max_wt_elev_m,
                group = 1,
                text = paste0("Date: ", datetime, "\n",
                              "Water Table Elevation (m): ", max_wt_elev_m))) + 
  theme_classic() + 
  labs(x = "Time", 
       y = "Water Table Elevation (m)", 
       title = paste0("S2 Bogwell Water Table Elevation (", min_year, "-", max_year, ")")
       ) + 
  theme(plot.title = element_text(hjust = 0.5),
          plot.subtitle = element_text(hjust = 0.5))

#interactive plot
ggplotly(p_s2,
         tooltip = "text")
s2_se_clean %>% 
  #rename columns 
  rename(wte = max_wt_elev_m) %>% 
  #remove rows that have NA wte values 
  filter(!is.na(wte)) %>% 
  summarise(mean_wte_m = round(mean(wte), digits = 3),
            max_wte_m = max(wte),
            min_wte_m = min(wte), 
            sd_wte_m = round(sd(wte), digits = 3)
            ) %>% 
  #t() %>% 
  kable(caption = "S2 Shaft Encoder Bogwell Statistics", 
        col.names = c("Mean WTE (m)", 
                      "Max WTE (m)",
                      "Min WTE (m)",
                      "SD")) %>% 
  kable_material(c("striped", "hover"))
S2 Shaft Encoder Bogwell Statistics
Mean WTE (m) Max WTE (m) Min WTE (m) SD
421.844 422.0916 421.4854 0.115

Note that this file is being knit as index.html into the climate_bogwell repository in order to update the GitHub pages website, where the plots can be viewed online.